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New method approximates complex Basset force in fluid dynamics using neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to approximate the Basset force within the Maxey-Riley-Gatignol equations, which model particle motion in fluids. This force, an integral term representing wake and boundary layer effects, significantly complicates numerical solutions by making the force dependent on a particle's past trajectory. The new approach utilizes universal differential equations and neural networks to transform the complex integral term into a system of ordinary differential equations, allowing for easier solving with standard numerical methods like Runge-Kutta. AI

IMPACT This research could enable more accurate simulations of particle dynamics in fluids, potentially impacting fields like computational fluid dynamics and materials science.

RANK_REASON Academic paper detailing a novel approximation method for a complex physics equation using machine learning techniques. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New method approximates complex Basset force in fluid dynamics using neural networks

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Academic paper detailing a novel approximation method for a complex physics equation using machine learning techniques. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Finn Sommer, Vamika Rathi, Sebastian Goetschel, Daniel Ruprecht ·

    Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations

    arXiv:2604.08194v2 Announce Type: replace Abstract: The Maxey-Riley-Gatignol equations (MaRGE) model the motion of spherical inertial particles in a fluid. They contain the Basset force, an integral term which models history effects due to the formation of wakes and boundary laye…

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